AI Object Detection in Video Streams for Real-Time Metadata
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Solution Overview
Problem
Users face significant time and effort in manually searching for information about objects in live and recorded video streams, requiring a shift in attention and prior knowledge, which limits their ability to engage with real-time information services related to identifiable objects.
Innovation Solution
The integration of machine learning and artificial intelligence image classification models within media streaming platforms to provide real-time, user-specific metadata streams, allowing users to interact with and receive information about identified objects directly within the video stream without prior knowledge, using MPEG-7 metadata streams and AI models like Tensorflow for image recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual searching methods are used to find information about objects in video streams, then users can obtain object information, but users experience significant time consumption and attention break from the video stream
Solution Approach 1:
The system performs preliminary actions by automatically detecting and identifying objects in the video stream before the user needs information. AI models continuously analyze video frames to recognize objects, prepare metadata, and make information readily available when users interact with the stream, eliminating the need for manual searching at the moment of interest.
Solution Approach 2:
The patent introduces an intermediary system consisting of AI object detection models and metadata generation components that mediate between the video stream and the user. This intermediary automatically processes video content, identifies objects, and provides information services, replacing manual searching and eliminating the need for users to break their focus from the video.
2Ease of operation
If manual searching is performed without prior knowledge, then users lack the necessary knowledge to successfully search, but requiring prior knowledge creates a barrier to accessing information
Solution Approach 1:
The system enables self-service by allowing users to simply interact with detected objects in the video stream without needing any prior knowledge. The AI system automatically handles object identification, information retrieval, and presentation, making the process as easy as clicking or selecting an object while watching the video.
Solution Approach 2:
The patent changes the parameter of user knowledge requirement from high to low by implementing automated AI-based object recognition. Instead of requiring users to know what to search for, the system automatically detects objects and provides information based on user interaction with the visual content, fundamentally changing the accessibility parameter.
3Adaptability or versatility
If AI classification models are integrated into the media streaming platform, then real-time object identification and personalized information services are enabled, but system complexity increases
Solution Approach 1:
The system segments the complex AI processing task into distinct components: object detection models, classification models, metadata generation services, and information delivery mechanisms. This segmentation allows each component to be developed, optimized, and managed independently, reducing overall system complexity while maintaining high adaptability.
Solution Approach 2:
The patent implements universal AI classification models that can identify multiple types of objects across different video streams and contexts. These multi-functional models reduce system complexity by replacing the need for separate specialized systems, while still providing personalized information services through a unified platform.
Data Source
AI summary
A media stream is accessed, and one or more classification models are selected for the media stream. Using the selected classification models, at least one object in the media stream is identified. An input associated with an identified object is received and, in response, information related to the object is generated for presentation.